Abstract

Nonwoven fabrics refer to materials with fibers orientated in a preferential or non-preferred direction within the structure and congregated by mechanical constraints such as bonding or self-entanglement of fibers. Polymeric fiber-based nonwovens are widely used in Health and Hygiene applications such as diapers, feminine napkins, and wipes. The mechanical properties of nonwoven sheets such as strength and strain at break are relevant to those applications. The present study aims to provide a fundamental understanding on the property relationship between individual fibers and nonwoven material with specific bonding patterns using finite element modeling. The investigation focuses on the validation of the prediction response when compared to experimental results and elucidating the deformation of the nonwoven material under tension. The model was validated to accurately predict the mechanical properties of the nonwoven sheet using the structural characteristics and fiber properties as the inputs. Comparative studies were subsequently carried out to study the influence of fiber orientation distribution, fiber dimensions, and the bonding pattern (bonding geometry, bonding density, bonding strength, etc.) on the mechanical properties of the nonwoven material. It was demonstrated that the mechanical strength of the nonwoven is proportional to properties of the fiber with the same bonding pattern and fiber orientation distribution, under the circumstance that the fiber properties do not alter the deformation mechanism of the nonwoven under loading. The outcome of the modeling work also reveals that, among all the parameters, the bonding pattern plays a significant role along with the fiber properties. With the employment of the developed modeling tool, a design of experiments (DOE) can be performed to optimize the bonding pattern and lead to better mechanical properties if the fiber properties are constrained. The study reveals that with manipulation of the bonding pattern for the nonwoven, the strength and strain at failure can be optimized within a factor of 2.